Numerous pretext tasks, also called pseudo tasks, have been developed for unsupervised representation learning. [1]}, [2]} predict image transformations. In [3]}, [4]}, spatial structure of the image is exploited to create pretext tasks. In [5]}, a model is trained by enforcing count consistency in image and its tiles. In [6]}, a model is iteratively trained to classify images based on the labels obtained using k-means clustering. In [7]}, a teacher network is trained on a hard pretext task and its knowledge is transferred to a student network via k-means clustering. For simplicity, we briefly evaluate the Jigsaw [4]} pretext task, but focus on the simpler, more effective pretext task of predicting rotations [2]}.
